Given that hypothesis testing holds the potential to provide keen business insights, the question that immediately arises is how does one conducts a hypothesis test? It is a five step process.
1. First we formulate the null hypothesis (Ho), the statement or claim that will be tested. Using our earlier widget example, the null hypothesis (Ho) would be “Productivity is low in the widget making department because morale is low” (Bushman, 2007).
2. Next we formulate the alternative hypothesis (Ha), the exact opposite of the null hypothesis. The alternative hypothesis (Ha) would be “productivity is unrelated to morale.”
3. We then identify a test statistic that can be used to measure the truth of the null hypothesis (more on this shortly).
4. We determine the P-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true; the lower the p-value, the less likely the result is if the null hypothesis is true, and consequently the more “significant” the result is, in the sense of statistical significance (Graphpad.
5. Finally, we assess the significance of the P-value.
Taking hypothesis testing away from the domain of widget making and into our core business of marketing food products, it becomes rapidly apparent how hypothesis testing can be of use. For example, we have developed a new line of snack food products and are currently working on creating packaging designed to attract first time buyers. The packages have been designed and shown to two groups of likely purchases. After viewing the packages, each person completed a questionnaire asking them about their feelings of excitement regarding the product that was shown in the.